How Simpledot Routes AI Questions Faster Without Wasting Model Calls

Not every question needs the same amount of AI work. A one-line calculation should not wait for a research swarm, while a high-stakes comparison should not be answered with the same lightweight path as a greeting. Simpledot’s latest routing work is built around that distinction: spend more compute where it improves the answer, and keep simple requests fast.

The old failure mode: every question looked complex

A routing system can become slow when broad words such as ‘which’, ‘best’, ‘also’ or ‘update’ are treated as proof that a user wants deep research. A short factual question may then trigger multiple model calls, a critic and a judge even when the user only asked for one line. That creates latency, cost and a poor first impression.

Classifying the task before choosing the depth

The improved router separates quick factual questions, coding, creative work, emotional support, decisions, research and multi-step tasks. It looks for stronger signals instead of matching common words in isolation. A genuine ‘Should I choose A or B?’ request can receive decision support, while an ordinary question containing ‘which’ can stay on the fast path.

Current facts still need live search

Speed does not mean pretending old knowledge is current. Questions containing signals such as latest, today, current, live, prices, weather, recent news or release updates can use the live-search path. Historical questions such as ‘Who was the first prime minister?’ do not need a current web lookup unless the user asks for sources or a modern update.

Complex work still gets deeper review

Long planning prompts, explicit comparisons, code tasks and research requests deserve more than a shallow answer. Those paths can use multiple model responses and judge or critic steps. The goal is not to reduce quality; it is to reserve deeper work for questions where another perspective can materially improve the result.

What this means for users

Simple questions should feel noticeably quicker. Complex questions may take longer because they are doing more work, but the system can now adapt its background judge wait to the answer size instead of showing a long fixed judging spinner. The result is a better balance between first-token speed, answer quality and responsible model usage.